Summary of Version Age-based Client Scheduling Policy For Federated Learning, by Xinyi Hu et al.
Version age-based client scheduling policy for federated learning
by Xinyi Hu, Nikolaos Pappas, Howard H. Yang
First submitted to arxiv on: 8 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A federated learning framework that enables collaborative training across multiple clients without sharing local data has been developed. This approach addresses communication bottlenecks by minimizing the number of clients that update their parameters upon each global aggregation, thereby reducing stragglers in the system. To achieve this, a novel concept called Version Age of Information (VAoI) is introduced, which considers both timeliness and content staleness to schedule client updates. By incorporating VAoI into the scheduling policy, the average version age can be minimized, resulting in more stable federated learning systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated learning lets many devices learn together without sharing their data. But it has a problem: some devices don’t get updated fast enough. This is called a “straggler.” To fix this, we created something new called Version Age of Information (VAoI). VAoI looks at how old the information is on each device and uses that to decide when to update them. By using VAoI, federated learning systems can be more stable and work better. |
Keywords
* Artificial intelligence * Federated learning
